Bibliographic record
Abstract
The concept of “loss aversion” in behavior economics was proposed by Kahneman and Tversky in 1979 with the famous prospect theory. Loss aversion is a cognitive bias suggesting that an avoidance of loss is preferred by people than an equivalent gain. The impact of loss aversion is profound in multiple areas of human life, including economics, society, politics, media, etc. This study aims to investigate the relation between loss aversion and trade policies, in particular, the implementation of protectionist trade policies. Literatures in the area of political economics about trade policy are reviewed. The Grossman-Helpman model, which predicts a set of determinants of trade policy, is discussed. Three empirical studies – the U.S. steel industry, the U.S. Section 301 proceedings, and the Chinese cotton industry – as well as the influence of loss aversion in each case, are analyzed and discussed in detail. Overall, the study draws its conclusion based on previous literatures and empirical studies, and it further confirms that loss aversion would lead to a risk-averse behavior of policy makers by reviewing empirical cases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".